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Comparing Prevalence Estimates From Population-Based Surveys to Inform Surveillance Using Electronic Health Records
INTRODUCTION: Electronic health record (EHR) systems provide an opportunity to use a novel data source for population health surveillance. Validation studies that compare prevalence estimates from EHRs and surveys most often use difference testing, which can, because of large sample sizes, lead to d...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Centers for Disease Control and Prevention
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5467464/ https://www.ncbi.nlm.nih.gov/pubmed/28595032 http://dx.doi.org/10.5888/pcd14.160516 |
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author | Tatem, Kathleen S. Romo, Matthew L. McVeigh, Katharine H. Chan, Pui Ying Lurie-Moroni, Elizabeth Thorpe, Lorna E. Perlman, Sharon E. |
author_facet | Tatem, Kathleen S. Romo, Matthew L. McVeigh, Katharine H. Chan, Pui Ying Lurie-Moroni, Elizabeth Thorpe, Lorna E. Perlman, Sharon E. |
author_sort | Tatem, Kathleen S. |
collection | PubMed |
description | INTRODUCTION: Electronic health record (EHR) systems provide an opportunity to use a novel data source for population health surveillance. Validation studies that compare prevalence estimates from EHRs and surveys most often use difference testing, which can, because of large sample sizes, lead to detection of significant differences that are not meaningful. We explored a novel application of the two one-sided t test (TOST) to assess the equivalence of prevalence estimates in 2 population-based surveys to inform margin selection for validating EHR-based surveillance prevalence estimates derived from large samples. METHODS: We compared prevalence estimates of health indicators in the 2013 Community Health Survey (CHS) and the 2013–2014 New York City Health and Nutrition Examination Survey (NYC HANES) by using TOST, a 2-tailed t test, and other goodness-of-fit measures. RESULTS: A ±5 percentage-point equivalence margin for a TOST performed well for most health indicators. For health indicators with a prevalence estimate of less than 10% (extreme obesity [CHS, 3.5%; NYC HANES, 5.1%] and serious psychological distress [CHS, 5.2%; NYC HANES, 4.8%]), a ±2.5 percentage-point margin was more consistent with other goodness-of-fit measures than the larger percentage-point margins. CONCLUSION: A TOST with a ±5 percentage-point margin was useful in establishing equivalence, but a ±2.5 percentage-point margin may be appropriate for health indicators with a prevalence estimate of less than 10%. Equivalence testing can guide future efforts to validate EHR data. |
format | Online Article Text |
id | pubmed-5467464 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Centers for Disease Control and Prevention |
record_format | MEDLINE/PubMed |
spelling | pubmed-54674642017-06-22 Comparing Prevalence Estimates From Population-Based Surveys to Inform Surveillance Using Electronic Health Records Tatem, Kathleen S. Romo, Matthew L. McVeigh, Katharine H. Chan, Pui Ying Lurie-Moroni, Elizabeth Thorpe, Lorna E. Perlman, Sharon E. Prev Chronic Dis Original Research INTRODUCTION: Electronic health record (EHR) systems provide an opportunity to use a novel data source for population health surveillance. Validation studies that compare prevalence estimates from EHRs and surveys most often use difference testing, which can, because of large sample sizes, lead to detection of significant differences that are not meaningful. We explored a novel application of the two one-sided t test (TOST) to assess the equivalence of prevalence estimates in 2 population-based surveys to inform margin selection for validating EHR-based surveillance prevalence estimates derived from large samples. METHODS: We compared prevalence estimates of health indicators in the 2013 Community Health Survey (CHS) and the 2013–2014 New York City Health and Nutrition Examination Survey (NYC HANES) by using TOST, a 2-tailed t test, and other goodness-of-fit measures. RESULTS: A ±5 percentage-point equivalence margin for a TOST performed well for most health indicators. For health indicators with a prevalence estimate of less than 10% (extreme obesity [CHS, 3.5%; NYC HANES, 5.1%] and serious psychological distress [CHS, 5.2%; NYC HANES, 4.8%]), a ±2.5 percentage-point margin was more consistent with other goodness-of-fit measures than the larger percentage-point margins. CONCLUSION: A TOST with a ±5 percentage-point margin was useful in establishing equivalence, but a ±2.5 percentage-point margin may be appropriate for health indicators with a prevalence estimate of less than 10%. Equivalence testing can guide future efforts to validate EHR data. Centers for Disease Control and Prevention 2017-06-08 /pmc/articles/PMC5467464/ /pubmed/28595032 http://dx.doi.org/10.5888/pcd14.160516 Text en https://creativecommons.org/licenses/by/4.0/This is a publication of the U.S. Government. This publication is in the public domain and is therefore without copyright. All text from this work may be reprinted freely. Use of these materials should be properly cited. |
spellingShingle | Original Research Tatem, Kathleen S. Romo, Matthew L. McVeigh, Katharine H. Chan, Pui Ying Lurie-Moroni, Elizabeth Thorpe, Lorna E. Perlman, Sharon E. Comparing Prevalence Estimates From Population-Based Surveys to Inform Surveillance Using Electronic Health Records |
title | Comparing Prevalence Estimates From Population-Based Surveys to Inform Surveillance Using Electronic Health Records |
title_full | Comparing Prevalence Estimates From Population-Based Surveys to Inform Surveillance Using Electronic Health Records |
title_fullStr | Comparing Prevalence Estimates From Population-Based Surveys to Inform Surveillance Using Electronic Health Records |
title_full_unstemmed | Comparing Prevalence Estimates From Population-Based Surveys to Inform Surveillance Using Electronic Health Records |
title_short | Comparing Prevalence Estimates From Population-Based Surveys to Inform Surveillance Using Electronic Health Records |
title_sort | comparing prevalence estimates from population-based surveys to inform surveillance using electronic health records |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5467464/ https://www.ncbi.nlm.nih.gov/pubmed/28595032 http://dx.doi.org/10.5888/pcd14.160516 |
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